Abstract: OBJECTIVE To construct the risk prediction models for nosocomial infection in elderly hospitalized patients of neurosurgery departments based on a decision tree Chi-Squared Automatic Interaction Detector (CHAID algorithm and a binary Logistic regression analysis and observe the result of prediction of the models. METHODS The patients who were hospitalized the neurosurgery department of the People's Hospital of Hainan Province from Jan 2018 to Jun 2019 and were aged no less than 60 years old were retrospectively analyzed, CHAID algorithm and Logistic regression analysis were employed to build the risk prediction models, and the prediction effects were evaluated and compared between the two types of models by means of area under curve (AUC) of receiver-operating-characteristic (ROC). RESULTS Of totally 1 111 patients who were enrolled in the study, 131 had nosocomial infection, with the incidence of infection 11.79%. Both CHAID and logistic regression analysis showed that the length of hospital stay no less than 31 days, use of ventilator and urinary tract intubation were the major influencing factors for the nosocomial infection. The accurate rate of risk prediction of the decision tree model was 88.2%, the model fit well; the test of goodness of fit of logistic regression model Hosmer-Lemeshow showed that the model fitted well too(χ²=9.690,P>0.05). The AUC of the decision tree model was 0.881(95%CI:0.861~0.899), the AUC of the logistic regression model was 0.880(95%CI:0.860~0.899), both of the models had medium prediction value, and there was no significant difference (Z＝0.188,P＞0.05). CONCLUSION The combination of the two models may facilitate the discovery of influencing factors for nosocomial infection in different levels and understanding of the relationship among the factors. The risk prediction models for nosocomial infection may provide guidance for prevention and control of the nosocomial infection.